Abstract

Because the pathologic processes that underlie Alzheimer's disease (AD) appear to
start 10 to 20 years before symptoms develop, there is currently intense interest
in developing techniques to accurately predict which individuals are most likely to
become symptomatic. Several AD risk prediction strategies - including identification
of biomarkers and neuroimaging techniques and development of risk indices that combine
traditional and non-traditional risk factors - are being explored. Most AD risk prediction
strategies developed to date have had moderate prognostic accuracy but are limited
by two key issues. First, they do not explicitly model mortality along with AD risk
and, therefore, do not differentiate individuals who are likely to develop symptomatic
AD prior to death from those who are likely to die of other causes. This is critically
important so that any preventive treatments can be targeted to maximize the potential
benefit and minimize the potential harm. Second, AD risk prediction strategies developed
to date have not explored the full range of predictive variables (biomarkers, imaging,
and traditional and non-traditional risk factors) over the full preclinical period
(10 to 20 years). Sophisticated modeling techniques such as hidden Markov models may
enable the development of a more comprehensive AD risk prediction algorithm by combining
data from multiple cohorts. As the field moves forward, it will be critically important
to develop techniques that simultaneously model the risk of mortality as well as the
risk of AD over the full preclinical spectrum and to consider the potential harm as
well as the benefit of identifying and treating high-risk older patients.

Commentary

There is a growing recognition that the complex pathologic cascade that leads to Alzheimer's
disease (AD) begins decades before the development of clinical symptoms [1]. This suggests that effective prevention will require predicting who will develop
AD decades before the onset of symptoms. Therefore, there has been growing interest
in developing accurate ways of identifying individuals who have an increased risk
of developing symptomatic AD so that they can be targeted for preventive interventions
such as risk factor reduction, behavioral modification, or pharmacologic treatment.

Much of the current work on AD prediction, including the Alzheimer's Disease Neuroimaging
Initiative (ADNI), has focused on identifying biomarkers and neuroimaging tests that
can accurately detect individuals with preclinical disease. These approaches implicitly
recognize the multifaceted nature of the AD pathologic cascade (amyloid beta deposition,
neurofibrillary tangles, neuronal dysfunction, and cerebral volume loss) [2] and rely on different tests to capture different aspects of that pathology. One recent
analysis of ADNI data [3] found that a combination of magnetic resonance imaging (MRI), cerebrospinal fluid,
and neuropsychological and functional markers predicted conversion from mild cognitive
impairment to AD with moderate accuracy (c statistic = 0.80). The c statistic, also
known as the area under the receiver operating characteristic curve, may range from
0 to 1, with 1 reflecting perfect discrimination and 0.5 reflecting no better than
chance.

An alternative approach to the prediction of AD risk has been to identify combinations
of traditional risk factors that can reliably differentiate individuals with a high
risk of developing AD from those with a low risk. One study found that mid-life risk
factors, including age, education, hypertension, hypercholesterolemia, and obesity,
predicted late-life dementia risk with moderate accuracy (c statistic = 0.77) [4]. Another study found that a combination of late-life factors, including age, cognitive
test scores, MRI measures, apolipoprotein E (APOE) genotype, cardiovascular disease,
and functional measures, also predicted dementia risk with moderate accuracy (c statistic
= 0.82) [5]. An abbreviated version that included age and simplified measures of cognitive function
and cardiovascular disease had moderate accuracy as well (c statistic = 0.77) [6].

Another recent study extended this work by noting that a tremendous range of factors
appear to predict AD, suggesting that AD is more likely in people with a broad decline
in health. Song and colleagues [7] found that 19 non-traditional risk factors such as a general health question ('How
good is your health?') and sensory questions ('How good is your eyesight or hearing?')
predicted which individuals were likely to develop AD, even after accounting for traditional
risk factors such as age, sex, education, cognitive function, and cardiovascular disease.
Implicit in this study is the assumption that, before leading to symptoms of cognitive
impairment, AD is associated with non-specific symptoms such as worse self-rated health.
However, the prognostic accuracy of this model was only fair (c statistic = 0.66).
In fact, in another study, demographics alone were found to be more predictive of
dementia risk (c statistic = 0.72), and significant increases in prognostic accuracy
were associated with the addition of APOE genotype (c statistic = 0.75) and vascular
risk factors (c statistic = 0.79) [8].

Taken together, these studies suggest that there are a variety of approaches for identifying
with fair to moderate levels of discrimination those individuals who are at high risk
of developing AD. However, all of these models suffer from two important limitations
in their approach to the prediction of AD risk.

First, none of these models explicitly accounts for the competing risk of death. AD
is predominantly a disease of older adults who experience high rates of death from
other causes. To effectively target preventive interventions - in particular, pharmacologic
interventions that may have adverse side effects - AD prediction models must identify
individuals who are likely to suffer symptomatic AD before death. Many risk factors
for AD are also risk factors for mortality (for example, older age, vascular risk
factors, and functional limitations) [9]. Therefore, some patients who are at high risk for AD may be at even higher risk
for death before symptomatic AD. To appropriately balance the potential benefit of
preventive intervention with the potential harm, AD prediction models must account
for death and identify individuals whose risk of AD outweighs their risk of death.

Second, dementia risk models developed to date have been developed in cohort studies
that focused on a limited range of potential predictors and had relatively short follow-up
periods (<10 years) or relatively narrow risk windows (for example, mid-life only
and late-life only). Thus, these studies have not examined the full range of AD predictors
(biomarkers, neuroimaging techniques, and traditional and non-traditional risk factors)
over the full 10-to 20-year preclinical risk period. The ideal study would include
repeated ascertainment of thousands of individuals over several decades, requiring
a tremendous investment in resources. Furthermore, these prospectively collected data
would not be available for decades, suggesting that alternative modeling techniques
need to be employed. One possible solution is to use hidden Markov models [10], which potentially could be used to combine data from multiple sources to model disease
state transitions across the full AD clinical-pathologic spectrum.

In conclusion, prediction of AD risk is a relatively new field of inquiry. Several
alternative approaches with moderate levels of accuracy have been developed, but none
is ready for widespread clinical use. As the field moves forward, it will be critically
important to develop techniques that simultaneously model the risk of mortality as
well as the risk of AD over the full preclinical spectrum and to consider the potential
harm as well as the benefit of identifying and treating high-risk older patients.

Abbreviations

Competing interests

The authors declare that they have no competing interests.

Acknowledgements

This work was supported in part by funding from the S. D. Bechtel, Jr. Foundation,
including an Early Investigator award (principal investigator: SJL), and the Program
for the Aging Century. SJL was also supported by a Paul Beeson Career Development
Award from the National Institute on Aging and the American Federation for Aging Research
(K23AG040779). Funders were not involved in the conception of this work, writing of
the manuscript, or the decision to submit the manuscript for publication.